The　Expectation　Maximization　(EM)　algorithm　is　widely　used　in　latent　variable　model　inference.　However,　when　data　are　distributed　across　various　locations,　directly　applying　the　EM　algorithm　can　often　be　impractical　due　to　communication　expenses　and　privacy　considerations.　To　address　these　challenges,　a　communication-efficient　distributed　EM　algorithm　is　proposed.　Under　mild　conditions,　the　proposed　estimator　achieves　the　same　mean　squared　error　bound　as　the　centralized　estimator.　Furthermore,　the　proposed　method　requires　only　one　extra　round　of　communication　compared　to　the　Average　estimator.　Numerical　simulations　and　a　real　data　example　demonstrate　that　the　proposed　estimator　significantly　outperforms　the　Average　estimator　in　terms　of　mean　squared　errors.